J. Med. Internet Res. - Evaluating a web-based clinical decision support system for language disorders screening in a nursery school.


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CKGROUND: Early and effective identification of developmental disorders during childhood remains a critical task for the international community. The second highest prevalence of common developmental disorders in children are language delays, which are frequently the first symptoms of a possible disorder.OBJECTIVE: This paper evaluates a Web-based Clinical Decision Support System (CDSS) whose aim is to enhance the screening of language disorders at a nursery school. The common lack of early diagnosis of language disorders led us to deploy an easy-to-use CDSS in order to evaluate its accuracy in early detection of language pathologies. This CDSS can be used by pediatricians to support the screening of language disorders in primary care.METHODS: This paper details the evaluation results of the "Gades" CDSS at a nursery school with 146 children, 12 educators, and 1 language therapist. The methodology embraces two consecutive phases. The first stage involves the observation of each child's language abilities, carried out by the educators, to facilitate the evaluation of language acquisition level performed by a language therapist. Next, the same language therapist evaluates the reliability of the observed results.RESULTS: The Gades CDSS was integrated to provide the language therapist with the required clinical information. The validation process showed a global 83.6% (122/146) success rate in language evaluation and a 7% (7/94) rate of non-accepted system decisions within the range of children from 0 to 3 years old. The system helped language therapists to identify new children with potential disorders who required further evaluation. This process will revalidate the CDSS output and allow the enhancement of early detection of language disorders in children. The system does need minor refinement, since the therapists disagreed with some questions from the CDSS knowledge base (KB) and suggested adding a few questions about speech production and pragmatic abilities. The refinement of the KB will address these issues and include the requested improvements, with the support of the experts who took part in the original KB development.CONCLUSIONS: This research demonstrated the benefit of a Web-based CDSS to monitor children's neurodevelopment via the early detection of language delays at a nursery school. Current next steps focus on the design of a model that includes pseudo auto-learning capacity, supervised by experts.

Resumo Limpo

ckground earli effect identif development disord childhood remain critic task intern communiti second highest preval common development disord children languag delay frequent first symptom possibl disorderobject paper evalu webbas clinic decis support system cdss whose aim enhanc screen languag disord nurseri school common lack earli diagnosi languag disord led us deploy easytous cdss order evalu accuraci earli detect languag patholog cdss can use pediatrician support screen languag disord primari caremethod paper detail evalu result gade cdss nurseri school children educ languag therapist methodolog embrac two consecut phase first stage involv observ child languag abil carri educ facilit evalu languag acquisit level perform languag therapist next languag therapist evalu reliabl observ resultsresult gade cdss integr provid languag therapist requir clinic inform valid process show global success rate languag evalu rate nonaccept system decis within rang children year old system help languag therapist identifi new children potenti disord requir evalu process will revalid cdss output allow enhanc earli detect languag disord children system need minor refin sinc therapist disagre question cdss knowledg base kb suggest ad question speech product pragmat abil refin kb will address issu includ request improv support expert took part origin kb developmentconclus research demonstr benefit webbas cdss monitor children neurodevelop via earli detect languag delay nurseri school current next step focus design model includ pseudo autolearn capac supervis expert

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